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"One of the teaching method is called his jigsaw method that can make the math makes lesson more interesting and increase the result of the test. In this method the student study in group and they solve problems together so that the teaching and learning process will be : exploration , elaboration, confrimation can be seen and applied . and the knowledge that they had got can be fixed in their mind."

"Di dunia kesehatan, tenaga medis dituntut untuk mengatasi berbagai jenis penyakit dengan gejala yang beragam. Oleh karena itu, diperlukan suatu teknologi untuk membantu mereka menyelesaikannya dengan baik. Penelitian ini mendukung mereka dengan menggunakan machine learning sebagai pemecah masalah. Metode machine learning yang digunakan pada penelitian ini adalah metode klasifikasi. Penulis membahas tentang stroke yang merupakan salah satu penyakit dengan angka kematian tertinggi di dunia. Penelitian ini mengamati perubahan densitas pada otak penderita stroke iskemik. Stroke iskemik merupakan salah satu jenis stroke yang terjadi ketika pembuluh darah tersumbat oleh trombus atau emboli. Penelitian ini menggunakan data CT scan dari Departemen Radiologi, Rumah Sakit Cipto Mangunkusumo, Jakarta, Indonesia. Data yang berupa citra CT scan diubah menjadi data numerik dengan metode Gray Level Co-Occurrence Matrix dan Local Binary Pattern yang dibandingkan performanya pada saat melakukan proses klasifikasi. Penelitian ini menerapkan Support Vector Machines sebagai metode klasifikasi. Didapatkan hasil bahwa Support Vector Machines dengan Local Binary Pattern menghasilkan performa yang lebih baik dibandingkan Support Vector Machines dengan Gray Level Co-Occurrence Matrix.

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In the health sector, the medical staffs are challenged to overcome many types of diseases with various symptoms. Therefore, a technology is needed to help them solving it well. This study is supporting them by using a machine learning as the problem solver. The machine learning method that is used in this study is classification method. The author discusses about stroke which is one of the diseases with the highest mortality rate in the world. This study observed the density changes in the brain of ischemic stroke sufferers. Ischemic stroke is one of the stroke types that occurs when the arteries are blocked by thrombus or embolism. This study used data of CT scan from Department of Radiology, Cipto Mangunkusumo Hospital, Jakarta, Indonesia. The image data of the CT scan is changed into a numerical data by using the Gray Level Co-Occurrence Matrix method and the Local Binary Pattern which is being compared when processing the classification. This study applies Support Vector Machines as the classification method. The results showed that Support Vector Machines with Local Binary Pattern has a better performance than Support Vector Machines with Gray Level Co-Occurrence Matrix."

"This classroom action research was held at SMAN I Koto Baru Dharmasrya with the aim is to improving student's motivation in laering mathematic through cooperative learning with two stay- two stray model plus hula hoop as a media."

"Vocatinal school is one of the high schools that has been choen as priority for students after junior high school. But when they are not able to continue their study , they can make job field and invite others to work together. But in teaching and learning process, teachers still gate some problem. so, the teacher should more creative to apply more interesting methods for strategy. One of model is introduced by the writer, cooperative learning that is guessing word type. It is hoped that the model will help students."

"Demam Berdarah Dengue (DBD) adalah penyakit yang disebabkan oleh virus dengue yang termasuk anggota genus Flavivirus dan keluarga Flaviviridae yang menyebar melalui nyamuk Aedes (Stegomyia). Jumlah kasus DBD di seluruh dunia dilaporkan meningkat lebih dari 8 kali lipat selama dua dekade terakhir, dari 505.430 kasus pada tahun 2000, menjadi lebih dari 2,4 juta pada tahun 2010, dan 5,2 juta pada tahun 2019. Peningkatan insiden DBD dipengaruhi oleh berbagai faktor, baik itu faktor alam, kebiasaan manusia, hingga jenis virus penyebab DBD itu sendiri. Faktor alam yang dimaksud disini antara lain faktor iklim, seperti curah hujan (mm), temperatur rata-rata (℃), dan kelembapan rata-rata (%). Peningkatan insiden DBD dapat dicegah dengan upaya pencegahan yang dilakukan sedini mungkin oleh pemerintah dan masyarakat. Informasi prediksi tren insiden DBD dapat menjadi masukan bagi masyarakat dan pemerintah untuk meningkatkan kewaspadaan terhadap peningkatan insiden DBD di DKI Jakarta. Dalam melakukan prediksi tren insiden DBD, dapat dilakukan dengan berbagai pendekatan, salah satu diantaranya adalah machine learning. Pada tugas akhir ini, implementasi machine learning menggunakan model The Attention Mechanism-Enhanced LSTM (LSTM-ATT) dan Poisson Regression, akan digunakan untuk memprediksi tren insiden DBD dari waktu ke waktu. LSTM-ATT adalah sebuah model machine learning yang digunakan untuk memproses data sequence. Poisson Regression adalah model regresi yang dapat digunakan pada data yang variabel responnya berdistribusi Poisson dan bertipe diskrit. Prediksi yang akan dilakukan didasarkan pada jumlah insiden DBD sebagai variabel respon, serta faktor iklim seperti curah hujan, suhu, dan kelembapan sebagai variabel prediktor. Untuk proporsi data, kedua model tersebut menggunakan proporsi data training sebesar 80% dan data testing sebesar 20%. Model yang dibentuk ini dievaluasi dengan nilai dari Root Mean Square Error (RMSE) dan Mean Absolute Error (MAE). Hasil implementasi terbaik pada skrips ini dihasilkan oleh model LSTM-ATT dengan evaluasi di setiap wilayah DKI Jakarta: Jakarta Pusat RMSE: 9,5727, MAE: 6,6946; Jakarta Timur RMSE: 21,5288, MAE: 15,6365; Jakarta Barat RMSE: 16,3683, MAE: 12,4908; Jakarta Utara RMSE: 23,5911, MAE: 15,2969; Jakarta Selatan RMSE: 18,3811, MAE: 14,0262.

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Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus, which belongs to the Flavivirus genus and Flaviviridae family, transmitted through Aedes mosquitoes (Stegomyia). The number of DHF cases worldwide has reported an increase of more than 8 times over the past two decades, from 505,430 cases in 2000 to over 2.4 million in 2010 and 5.2 million in 2019. The increase in DHF incidence is influenced by various factors, including natural factors, human behavior, and the type of dengue virus itself. Natural factors include climate factors such as rainfall (mm), average temperature (℃), and average humidity (%). The increase in DHF incidence can be prevented through early prevention efforts by the government and the community. Predicting the trend of DHF incidence can provide input for the community and the government to increase vigilance against the increasing incidence of DHF in Jakarta. Various approaches can be used for predicting the trend of DHF incidence, one of which is machine learning. In this final project, the implementation of machine learning using the LSTM-ATT and Poisson Regression models will be used to predict the trend of DHF incidence over time. LSTM-ATT is a machine learning model used to process sequential data, such as time series data. On the other hand, Poisson Regression is a regression model that can be used for data with Poisson-distributed response variables and discrete types. The prediction will be based on the number of DHF incidents as the dependent variable, and climate factors such as rainfall, temperature, and humidity as independent variables. The performance of the models will be evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The best implementation results in this thesis were produced by the LSTM-ATT model with evaluation in each area of DKI Jakarta: Central Jakarta RMSE: 9.5727, MAE: 6.6946; East Jakarta RMSE: 21.5288, MAE: 15.6365; West Jakarta RMSE: 16.3683, MAE: 12.4908; North Jakarta RMSE: 23.5911, MAE: 15.2969; South Jakarta RMSE: 18.3811, MAE: 14.0262."

"Demam Berdarah Dengue (DBD) masih menjadi masalah kesehatan yang utama di Indonesia.  Berdasarkan data dari Kemenkes RI, pada tahun 2022 jumlah insiden DBD dicatat sebanyak 131.265 yang mana sekitar 40% adalah anak-anak usia 0 sampai 14 tahun dengan jumlah kasus kematian mencapai 1.135 jiwa dengan 73% terjadi pada anak-anak usia 0 sampai 14 tahun. DBD disebabkan oleh virus dengue yang disebarkan melalui gigitan nyamuk Aedes aegypti  dan Aedes albopictus.. Selain faktor kebersihan lingkungan dan kebiasaan masyarakat, tingginya insiden DBD di Indonesia juga dipengaruhi oleh beberapa faktor iklim seperti curah hujan, temperatur, dan kelembapan. Memaksimalkan proses pencegahan DBD oleh pemerintah dan masyarakat dapat menekan tingginya kasus DBD di Indonesia. Salah satu cara untuk memaksimalkan proses pencegahan DBD adalah dengan melakukan prediksi jumlah insiden DBD yang akan terjadi kedepannya. Dengan mengetahui hasil prediksi jumlah insiden DBD, diharapkan masyarakat dan pemerintah dapat memaksimalkan proses pencegahan DBD. Pada tugas akhir ini, dilakukan prediksi jumlah insiden DBD menggunakan convolutional neural network dan extreme gradient boosting, dengan jumlah insiden sebelumnya dan faktor cuaca sebelumnya yang terdiri dari temperatur, curah hujan, dan kelembapan relatif sebagai variabel prediktor. Variabel prediktor yang digunakan ditentukan berdasarkan time lag dari masing-masing variabel prediktor terhadap jumlah insiden DBD menggunakan korelasi silang. Model convolutinal neural network dan extreme gradient boosting yang dibentuk dievaluasi dan dibandingkan berdasarkan nilai Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan waktu simulasi. Pada tugas akhir ini, convolutional neural network memberikan performa yang lebih baik dibandingkan dengan extreme gradient boosting berdasarkan nilai RMSE dan MAE dengan rata-rata 13,3586 untuk RMSE dan 9,2249 untuk MAE. Berdasarkan waktu simulasi, extreme gradient boosting memberikan performa yang lebih cepat dibandingkan convolutional neural network.

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Dengue Hemorrhagic Fever (DHF) remains a major health problem in Indonesia. Based on data from the Ministry of Health of Indonesia, in 2022, the number of DHF incidents recorded was 131,265, of which approximately 40% were children aged 0 to 14 years, with a total of 1,135 deaths, 73% of which occurred in children aged 0 to 14 years. DHF is caused by the dengue virus, which is transmitted through the bites of Aedes aegypti and Aedes albopictus mosquitoes. In addition to environmental cleanliness and societal habits, the high incidence of DHF in Indonesia is also influenced by several climate factors such as rainfall, temperature, and humidity. Maximizing the DHF prevention process by the government and the community can help reduce the number of DHF cases in Indonesia. One way to maximize the DHF prevention process is by predicting the future number of DHF incidents. By knowing the predicted number of DHF incidents, it is hoped that the community and the government can maximize the DHF prevention process. In this final project, the prediction of the number of DHF incidents is carried out using convolutional neural network and extreme gradient boosting, with the previous incident counts and previous weather factors consisting of temperature, rainfall, and relative humidity as predictor variables. The predictor variables used are determined based on the time lag of each predictor variable on the number of DHF incidents using cross-correlation. In this final project, the convolutional neural network outperforms extreme gradient boosting based on the RMSE and MAE values, with an average of 13.3586 for RMSE and 9.2249 for MAE. However, in terms of simulation time, extreme gradient boosting demonstrates faster performance compared to the convolutional neural network."

"ABSTRAKDi Indonesia, salah satu penyebab tingginya biaya BBM adalah adanya tindak pencuriandan penyelewengan BBM yang sering kali terjadi di tengah lautan. Hal ini bisa terjadikarena pada saat di tengah lautan, segala kegiatan kapal tersebut tidak bisa dipantau olehpusat operasional manajemen kapal. Selain upaya hukum, upaya pengawasan kapalmelalui teknologi terbaru juga terus dilakukan, salah satunya adalah teknologi VesselMonitoring System (VMS) berbasis Machine to machine (M2M). Perkembanganteknologi VMS dan telemetri telah memungkinkan pengawasan kondisi mesin danpemakaian BBM kapal yang sedang berlayar secara online dan real time. Denganmenambah perangkat pengukuran pemakaian bahan bakar tersebut, diharapkanmeningkatkan kecepatan koordinasi dan penanganan di lapangan saat terjadiketidakwajaran pemakaian BBM. Kecepatan dalam mengetahui adanya ketidakwajaranini sangat penting, karena proses pencurian minyak sering kali dilakukan dalam waktusingkat. Pencurian minyak dengan modus ilegal tapping di darat hanya memerlukanwaktu 15 menit untuk 2000 liter (2 ton) BBM, sementara di laut diperlukan sekitar 5 jamuntuk memindahkan 12 ton BBM, atau sekitar 2.4 ton per jam untuk sebuah kapal saja.Masalahnya untuk mengetahui ketidakwajaran tersebut masih tergantung pada analisatenaga ahli yang memerlukan waktu yang lama untuk melakukan analisa berbagaiparameter telemetri yang ada. Berdasarkan kondisi di atas, penelitian ini melakukananalisis statistik terhadap data telemetri terutama data pergerakan kapal dan aktivitasmesin untuk menentukan koefisien pergerakan kapal, lalu merancang sistempengklasifikasi kewajaran pemakaian BBM dengan metode Naive Bayes dan LogisticRegression. Metode ini dipilih karena bisa memberikan hasil yang baik untuk prediksidata-­data numerik maupun diskrit. Hasil penelitian ini menunjukkan bahwa data telemetridari sistem VMS dapat digunakan untuk mendeteksi adanya ketidakwajaran pemakaianBBM. Untuk kebutuhan klasifikasi kewajaran pemakaian BBM pada data telemetri kapal,algoritma pengklasifikasi Naive Bayes memiliki akurasi hingga 92% pada data sampeldan Logistic Regression mampu mendeteksi dengan akurasi hingga 96% pada datasampel.

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ABSTRACTIn Indonesia, one of the causes of high fuel costs is the occurrence of theft and misuse offuel which often occurs in the middle of the ocean. This can happen because when in themiddle of the ocean, all the activities of the ship cannot be monitored by the shipmanagement operational center. In addition to legal efforts, efforts to monitor shipsthrough the latest technology are also being carried out, one of which is the Machine toMachine (M2M) Vessel Monitoring System (VMS) technology. The development ofVMS and telemetry technology has enabled monitoring of engine conditions and fuelconsumption of ships that are sailing online and real time. By adding the fuel consumptionmeasurement device, it is expected to increase the speed of coordination and handling inthe field when there is an irregularity in the use of fuel. Speed in knowing the existenceof this irregularity is very important, because the process of oil theft is often done in ashort time. Theft of oil by illegal tapping on land only takes 15 minutes for 2000 liters (2tons) of fuel, while at sea it takes around 5 hours to move 12 tons of fuel, or around 2.4tons per hour for a ship. The problem is to find out the irregularities that still depend onthe analysis of experts who need a long time to analyze various parameters of existingtelemetry. Based on the above conditions, this study conducted a statistical analysis oftelemetry data, especially ship movement data and machine activity to determine thecoefficient of ship movements, then designed the fuel usage irregularity classificationsystem with the Naive Bayes and Logistics Regression. This method was chosen becauseit can provide good results for predicting numerical and discrete data. The results of thisstudy indicate that telemetry data from the VMS system can be used to detect anyirregularities in using BBM. For the needs of the fairness classification of BBM usage onship telemetry data, the Naive Bayes classification algorithm has an accuracy of up to92% in sample data and Logistic Regression is able to detect with accuracy up to 96% insample data."

"Inhibitor DPP-4 adalah pendekatan baru yang menjanjikan untuk pengobatan diabetes tipe-2 dengan risiko rendah hipoglikemia. Pemodelan hubungan kuantitatif struktur-aktivitas (QSAR) adalah pemodelan yang digunakan untuk menyaring basis data besar suatu senyawa untuk menentukan sifat biologis molekul kimia berdasarkan struktur kimianya. Pada tesis ini pemodelan QSAR yang digunakan adalah QSAR klasifikasi dan QSAR regresi. Sebelum membuat model QSAR akan melakukan esktraksi ciri pada struktur molekul (SMILES). Hasil ekstraksi ciri tersebut kemudian akan digunakan sebagai masukan untuk metode rotation forest kasus klasifikasi dan kasus regresi. Model QSAR klasifikasi akan memprediksi molekul aktif dan tidak aktif pada inhibitor DPP-IV. Sedangkan model QSAR regresi akan memprediksi nilai aktivitas IC50 inhibitor DPP-IV. Pada penelitian ini untuk kasus klasifikasi dan regresi juga membandingkan performa model rotation forest menggunakan matriks rotasi PCA dengan rotation forest menggunakan matriks rotasi Sparse PCA. Hasil penelitian ini menunjukkan bahwa model QSAR regresi menggunakan rotation forest dengan matriks rotasi PCA (RFR(PCA)) memperoleh koefisien korelasi kuadrat 29.2% dengan RMSE 45%. Sementara itu, menggunakan rotation forest dengan matriks rotasi Sparse PCA (RFR(SPCA)) memperoleh koefisien korelasi kuadrat 27.1% dengan RMSE 45.6%. Pada QSAR klasifikasi persentase banyaknya molekul yang aktif sangat besar dibandingkan yang molekul tidak aktif, hal ini dapat menyebabkan nilai evaluasi berbeda. SMOTE (Synthetic Minority Oversampling Technique) merupakan salah satu metode untuk menangani data tidak seimbang tersebut dengan cara membangkitkan data buatan. Hasil penelitian ini menunjukkan bahwa model QSAR klasifikasi menggunakan rotation forest dengan matriks rotasi PCA (RFC(PCA)) memperoleh performa tertinggi dalam memprediksi molekul aktif dan tidak aktif, yaitu nilai MCC 77.7% dengan nilai akurasi sebesar 89%, sensitivitas 89.6%, dan spesifisitas 88.1%. Sementara itu, model QSAR klasifikasi menggunakan rotation forest dengan matriks rotasi SPCA (RFC(SPCA)) memperoleh performa tertinggi, yaitu nilai MCC 80.9% dengan nilai akurasi sebesar 90.5%, sensitivitas 90.8%, dan spesifisitas 90.2%.

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DPP-4 inhibitors are a new approach for the treatment of type 2 diabetes with a low risk of hypoglycemia. The Quantitative Structure-Activity Relationship (QSAR) model is a model used to filter large databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The QSAR modeling that is used in this research is QSAR classification and QSAR regression. Before creating the model, QSAR will perform feature extraction on the molecular structure (SMILES). The results of the feature extraction will be used as inputs for the rotation forest method of the classification and regression cases. The QSAR classification model predicts active and inactive molecules in DPP-IV inhibitors, while the regression QSAR model predicts the value of IC50 DPP-IV inhibitor activity. In this study, the classification and regression cases are also comparing the performances between the rotation forest model using the PCA rotation matrix and the rotation forest model using the Sparse PCA rotation matrix. 

The results of this study indicate that the QSAR regression model using rotation forest with the rotation matrix PCA (RFR (PCA)) obtained a squared correlation coefficient of 29.2% with RMSE 45%. Meanwhile, using rotation forest regression with the Sparse PCA (RFR (SPCA)) rotation matrix obtained a quadratic correlation coefficient of 27.1% with RMSE 45.6%. In the QSAR classification, the percentage of active molecules is very large compared to inactive molecules, this can cause different evaluation values. SMOTE (Synthetic Minority Oversampling Technique) is one method for handling such unbalanced data by generating artificial data. The results of this study indicate that the classification QSAR model using rotation forest classification with PCA (RFC (PCA)) rotation matrix obtained the highest performance in predicting active and inactive molecules as follows: MCC value of 77.7% with an accuracy value of 89%, sensitivity value of 89.6% and specificity value of 88.1%. Meanwhile, the QSAR classification model using rotation forest classification with the SPCA rotation matrix (RFC (SPCA)) obtained the highest performance as follows: MCC value of 80.9% with an accuracy value of 90.5%, sensitivity value of 90.8%, and specificity value of 90.2%."

"This study aims to identify the effectiveness of cooperative learning of Jigsaw and STAD models at elementary school students. Specifically this study aims to (I) identify the learning activities of students in Jigsaw and STAD model, and (ii) identify the effect of cooperative learning on student outomes, and (iii) identify the effects of cooperative learning on social skill on elementary school students. Subjects are fourth grade elementary school student of SD Sambungan 01 and 02 in Sub District of Undaan in Kudus Regency, was randomly selected through cluster sampling technique includes two experimental groups. It consists of 20 students for STAD model and 21 students for Jigsaw model. Collecting the data in the form of student learning outcomesn conducted during the 2009.1 academic year through tests and non test. Data was analyzed using ANOVA and testing requirements through the normality test and homogeneity of variance. Data processing activities and skills gained through observation and then be processed through descriptive analysis. Research results revealed that the implementation of cooperative learning model of Jigsaw and STAD model was capable to improve student learning activities. While the use of both models show that students only skillful in capturing the concept. The value of the influence of process skills with Jigsaw model is 59.6% and with the STAD model is 55.5%. Average yield study showed significance differences that Jigsaw model is better than STAD model. This means that the Jigsaw model is more able to improve student learning activity compared with STAD model. It is concluded that the Jigzaw model is better than STAD model. Teachers are advised to apply Jigsaw method of cooperative learning model as an alternative to teaching in the classroom."

"one or factors that determine the success of education process in classroom is teacher. Teachers not only has role as teacher that transfer knowledge to the students but also as a model. The are various ways conducted by the techers to improve students study result."